Complex disorders typically involve multiple factors and gene loci and are heavily influenced by environmental conditions. This is particularly true for metabolic diseases and diseases affecting mental health. As is often the case with such illnesses, regulators of gene expression, as well as their specific target genes can play a major role in not just the etiology but also the treatment of the disease. Some of the best characterized regulators associated with human disease are members of the nuclear receptor superfamily -- transcription factors that are regulated by the binding of ligands that are often influenced by environmental conditions. However, while a great deal is known about the mechanism by which individual nuclear receptors regulate the expression of individual target genes, what is sorely lacking is a comprehensive view of all their potential target genes in all the tissues in which they are expressed. This is true not only for metabolic organs for which the role of nuclear receptors is fairly well studied, but also the CNS where much less is known about the target genes and the functions of the nuclear receptors. Furthermore, several nuclear receptors are very effective drug targets, for both metabolic and mental disorders, making them among the most clinically relevant of all the transcription factors. Whereas recent advances in whole genome expression profiling and genome-wide location analysis (ChIP-chip, ChIP-seq) have allowed us to begin to define the complete transcriptome for some of these factors, much more remains to be done. Furthermore, these approaches can be technically challenging and costly, and hence very limiting. A complementary approach that has not been fully exploited is a computational one based on the DNA response elements that recruit the nuclear receptors to the regulatory regions of their target genes. However, in order to take full advantage of this approach, one must first have a comprehensive dataset of the binding motifs to which the receptors bind. The goals of this proposal are two-fold: 1) to comprehensively define the DNA binding specificity of a critical group of nuclear receptors using high throughput technology;and 2) to use that data to mine existing datasets in order to associate those nuclear receptors with human disease. These goals will be accomplished by pursuing three specific aims: 1) Use protein binding microarrays (PBMs) to determine the DNA binding specificity of select nuclear receptors on 10's of 1000's of unique sequences;and then use that data to develop high accuracy computational models to predict the entire set of sequences to which a given nuclear receptor binds;2) use the PBM data and models generated in Aim 1 to computationally identify all the potential binding sites, target genes and related SNPs in the human genome for each nuclear receptor;and cross reference the results with databases linking genes and SNPs to human diseases (i.e., GAD and HapMap);3) incorporate the results into a network of nuclear receptors and their target genes, with particular emphasis on metabolic diseases and mental health disorders. All of the binding motifs, potential target genes, related SNPs and networks will be catalogued in the on-line resource PAZAR, a public database of transcription factor and regulatory sequence annotation (http://www.pazar.info/cgi-bin/index.pl), and the NIH-funded Nuclear Receptor Signaling Atlas (NURSA) (http://www.nursa.org/). Many chronic human diseases that arise later in life - such as diabetes, atherosclerosis and mental disorders - are due to multiple factors, both genetic and environmental. The recent sequencing of the human genome has allowed us to identify new genes associated with these diseases at an ever increasing rate. In this study we apply the latest high throughput technology to help identify variations in the genetic sequence that might be related to those (and other) diseases by examining the regulatory regions of genes and the proteins that bind those regions. Our work will help bring us closer to an era of personalized medicine in which prevention, diagnosis and treatment are tailored to the individual patient, making them more effective and less costly.